OpenRaiser/Intern-Atlas huggingface dataset:
Finite Element Modeling of Surface Traveling Wave Friction Driven for Rotary Ultrasonic Motor
Venue: arXiv cs.RO 2024
Year: 2024

Finite element modeling (FEM) is a critical tool in the design and analysis of piezoelectric devices, offering detailed numerical simulations that guide various applications. While traditionally applied to eigenfrequency analysis and time-dependent studies for predicting excitation eigenfrequencies and estimating traveling wave amplitudes, FEM's potential extends to more sophisticated tasks. Advanced FEM applications, such as modeling friction-driven dynamic motion and reaction forces, are essential for accurately simulating the complex behaviors of piezoelectric actuators under real-world conditions. This paper presents a comprehensive motor model that encompasses the coupling dynamics between the stator and rotor in a piezoelectric ultrasonic motor (USM). Utilizing contact theory, the model simulates the complex conditions encountered during the USM's initial start-up phase and its transition to steady-state operation. Implemented in COMSOL Multiphysics, the model provides an in-depth analysis of a rotary piezoelectric actuator, capturing the dynamic interactions and reaction forces that influence its performance. The introduction of this FEM-based model represents a significant advancement in the simulation and understanding of piezoelectric actuators. By offering a more complete picture of the motor's behavior from start-up to steady state, this study enables more accurate control and optimization of piezoelectric devices, enhancing their efficiency and reliability in practical applications.
Resilient UAVs Location Sharing Service Based on Information Freshness and Opportunistic Deliveries
Venue: arXiv cs.NI 2024
Year: 2024

Unmanned aerial vehicles (UAV) have been recognized as a versatile platform for a wide range of services. During the flight, these vehicles must avoid collisions to operate safely. In this way, they demand to keep spatial awareness, i.e., to know others in their coverage area. However, mobility and positioning aspects hamper building UAV network infrastructure to support reliable basic services. Thus, such vehicles call for a location service with up-to-date information resilient to false location injection threats. This work proposes FlySafe, a resilient UAVs location sharing service that employs opportunistic approaches to deliver UAVs' location. FlySafe takes into account the freshness of UAVs' location to maintain their spatial awareness. Further, it counts on the age of the UAV's location information to trigger device discovery. Simulation results showed that FlySafe achieved spatial awareness up to 94.15% of UAV operations, being resilient to~false locations injected in the network. Moreover, the accuracy in device discovery achieved 94.53% with a location error of less than 2 m.
Uncovering the Secrets of Human-Like Movement: A Fresh Perspective on Motion Planning
Venue: arXiv cs.RO 2024
Year: 2024

This article explores human-like movement from a fresh perspective on motion planning. We analyze the coordinated and compliant movement mechanisms of the human body from the perspective of biomechanics. Based on these mechanisms, we propose an optimal control framework that integrates compliant control dynamics, optimizing robotic arm motion through a response time matrix. This matrix sets the timing parameters for joint movements, turning the system into a time-parameterized optimal control problem. The model focuses on the interaction between active and passive joints under external disturbances, improving adaptability and compliance. This method achieves optimal trajectory generation and balances precision and compliance. Experimental results on both a manipulator and a humanoid robot validate the approach.
Semantics Preserving Emoji Recommendation with Large Language Models
Venue: arXiv cs.CL 2024
Year: 2024

Emojis have become an integral part of digital communication, enriching text by conveying emotions, tone, and intent. Existing emoji recommendation methods are primarily evaluated based on their ability to match the exact emoji a user chooses in the original text. However, they ignore the essence of users' behavior on social media in that each text can correspond to multiple reasonable emojis. To better assess a model's ability to align with such real-world emoji usage, we propose a new semantics preserving evaluation framework for emoji recommendation, which measures a model's ability to recommend emojis that maintain the semantic consistency with the user's text. To evaluate how well a model preserves semantics, we assess whether the predicted affective state, demographic profile, and attitudinal stance of the user remain unchanged. If these attributes are preserved, we consider the recommended emojis to have maintained the original semantics. The advanced abilities of Large Language Models (LLMs) in understanding and generating nuanced, contextually relevant output make them well-suited for handling the complexities of semantics preserving emoji recommendation. To this end, we construct a comprehensive benchmark to systematically assess the performance of six proprietary and open-source LLMs using different prompting techniques on our task. Our experiments demonstrate that GPT-4o outperforms other LLMs, achieving a semantics preservation score of 79.23%. Additionally, we conduct case studies to analyze model biases in downstream classification tasks and evaluate the diversity of the recommended emojis.
A variational approach to geometric mechanics for undulating robotic locomotion
Venue: arXiv cs.RO 2024
Year: 2024

Limbless organisms of all sizes use undulating patterns of self-deformation to locomote. Geometric mechanics, which maps deformations to motions, provides a powerful framework to formalize and investigate the theoretical properties and limitations of such modes of locomotion. However, the inherent level of abstraction poses a challenge when bridging the gap between theory or simulations and laboratory experiments. We investigate the challenges of modeling motion trajectories of an undulating robotic locomotor by comparing experiments and simulations performed with a variational integrator. Despite the extensive simplifications that the model based on a geometric variation principle entails, the simulations show good agreement on average. Notably, our approach merely requires the knowledge of the \emph{dissipation metric} -- a Riemannian metric on the configuration space, which can in practice be approximated by means closely resembling \emph{resistive force theory}.
An Anti-disguise Authentication System Using the First Impression of Avatar in Metaverse
Venue: arXiv cs.CR 2024
Year: 2024

Metaverse is a vast virtual world parallel to the physical world, where the user acts as an avatar to enjoy various services that break through the temporal and spatial limitations of the physical world. Metaverse allows users to create arbitrary digital appearances as their own avatars by which an adversary may disguise his/her avatar to fraud others. In this paper, we propose an anti-disguise authentication method that draws on the idea of the first impression from the physical world to recognize an old friend. Specifically, the first meeting scenario in the metaverse is stored and recalled to help the authentication between avatars. To prevent the adversary from replacing and forging the first impression, we construct a chameleon-based signcryption mechanism and design a ciphertext authentication protocol to ensure the public verifiability of encrypted identities. The security analysis shows that the proposed signcryption mechanism meets not only the security requirement but also the public verifiability. Besides, the ciphertext authentication protocol has the capability of defending against the replacing and forging attacks on the first impression. Extensive experiments show that the proposed avatar authentication system is able to achieve anti-disguise authentication at a low storage consumption on the blockchain.
Adaptive Large Language Models By Layerwise Attention Shortcuts
Venue: arXiv cs.CL 2024
Year: 2024

Transformer architectures are the backbone of the modern AI revolution. However, they are based on simply stacking the same blocks in dozens of layers and processing information sequentially from one block to another. In this paper, we propose to challenge this and introduce adaptive computations for LLM-like setups, which allow the final layer to attend to all of the intermediate layers as it deems fit through the attention mechanism, thereby introducing computational \textbf{attention shortcuts}. These shortcuts can thus make the architecture depth and context adaptive. We showcase four different datasets, namely acoustic tokens, natural language, and symbolic music, and we achieve superior performance for GPT-like architecture. We give evidence via attention maps that the models learn complex dependencies across layers that are adaptive in context and depth depending on the input tokens.
An Exploration of Effects of Dark Mode on University Students: A Human Computer Interface Analysis
Venue: arXiv cs.HC 2024
Year: 2024

This research dives into exploring the dark mode effects on students of a university. Research is carried out implementing the dark mode in e-Learning sites and its impact on behavior of the users. Students are spending more time in front of the screen for their studies especially after the pandemic. The blue light from the screen during late hours affects circadian rhythm of the body which negatively impacts the health of humans including eye strain and headache. The difficulty that students faced during the time of interacting with various e-Learning sites especially during late hours was analyzed using different techniques of HCI like survey, interview, evaluation methods and principles of design. Dark mode is an option which creates a pseudo inverted adaptable interface by changing brighter elements of UI into a dim-lit friendly environment. It is said that using dark mode will lessen the amount of blue light emitted and benefit students who suffer from eye strain. Students' interactions with dark mode were investigated using a survey, and an e-learning site with a dark mode theme was created. Based on the students' comments, researchers looked into the effects of dark mode on HCI in e-learning sites. The findings indicate that students have a clear preference for dark mode: 79.7% of survey participants preferred dark mode on their phones, and 61.7% said they would be interested in seeing this feature added to e-learning websites.
A Physics Informed Neural Network (PINN) Methodology for Coupled Moving Boundary PDEs
Venue: arXiv cs.LG 2024
Year: 2024

Physics-Informed Neural Network (PINN) is a novel multi-task learning framework useful for solving physical problems modeled using differential equations (DEs) by integrating the knowledge of physics and known constraints into the components of deep learning. A large class of physical problems in materials science and mechanics involve moving boundaries, where interface flux balance conditions are to be satisfied while solving DEs. Examples of such systems include free surface flows, shock propagation, solidification of pure and alloy systems etc. While recent research works have explored applicability of PINNs for an uncoupled system (such as solidification of pure system), the present work reports a PINN-based approach to solve coupled systems involving multiple governing parameters (energy and species, along with multiple interface balance equations). This methodology employs an architecture consisting of a separate network for each variable with a separate treatment of each phase, a training strategy which alternates between temporal learning and adaptive loss weighting, and a scheme which progressively reduces the optimisation space. While solving the benchmark problem of binary alloy solidification, it is distinctly successful at capturing the complex composition profile, which has a characteristic discontinuity at the interface and the resulting predictions align well with the analytical solutions. The procedure can be generalised for solving other transient multiphysics problems especially in the low-data regime and in cases where measurements can reveal new physics.
An Enhanced Online Certificate Status Protocol for Public Key Infrastructure with Smart Grid and Energy Storage System
Venue: arXiv cs.CR 2024
Year: 2024

The efficiency of checking certificate status is one of the key indicators in the public key infrastructure (PKI). This prompted researchers to design the Online Certificate Status Protocol (OCSP) standard, defined in RFC 6960, to guide developers in implementing OCSP components. However, as the environment increasingly relies on PKI for identity authentication, it is essential to protect the communication between clients and servers from rogue elements. This can be achieved by using SSL/TLS techniques to establish a secure channel, allowing Certificate Authorities (CAs) to safely transfer certificate status information. In this work, we introduce the OCSP Stapling approach to optimize OCSP query costs in our smart grid environment. This approach reduces the number of queries from the Device Language Message Specification (DLMS) server to the OCSP server. Our experimental results show that OCSP stapling increases both efficiency and security, creating a more robust architecture for the smart grid.
Inside Alameda Research: A Multi-Token Network Analysis
Venue: arXiv cs.SI 2024
Year: 2024

We analyze the token transfer network on Ethereum, focusing on accounts associated with Alameda Research, a cryptocurrency trading firm implicated in the misuse of FTX customer funds. Using a multi-token network representation, we examine node centralities and the network backbone to identify critical accounts, tokens, and activity groups. The temporal evolution of Alameda accounts reveals shifts in token accumulation and distribution patterns leading up to its bankruptcy in November 2022. Through network analysis, our work offers insights into the activities and dynamics that shape the DeFi ecosystem.
Towards Effective User Attribution for Latent Diffusion Models via Watermark-Informed Blending
Venue: arXiv cs.MM 2024
Year: 2024

Rapid advancements in multimodal large language models have enabled the creation of hyper-realistic images from textual descriptions. However, these advancements also raise significant concerns about unauthorized use, which hinders their broader distribution. Traditional watermarking methods often require complex integration or degrade image quality. To address these challenges, we introduce a novel framework Towards Effective user Attribution for latent diffusion models via Watermark-Informed Blending (TEAWIB). TEAWIB incorporates a unique ready-to-use configuration approach that allows seamless integration of user-specific watermarks into generative models. This approach ensures that each user can directly apply a pre-configured set of parameters to the model without altering the original model parameters or compromising image quality. Additionally, noise and augmentation operations are embedded at the pixel level to further secure and stabilize watermarked images. Extensive experiments validate the effectiveness of TEAWIB, showcasing the state-of-the-art performance in perceptual quality and attribution accuracy.
Gestalt driven augmented collimator widget for precise 5 dof dental drill tool positioning in 3d space
Venue: arXiv cs.HC 2024
Year: 2024

Drill tool positioning in dental implantology is a challenging task requiring 5DOF precision as the rotation around the tool axis is not influential. This work improves the quasi-static visual elements of the state-of-the-art with a novel Augmented Collimation Widget (ACW), an interactive tool of position and angle error visualization based on the gestalt reification, the human ability to group geometric elements. The user can seek in a quick, pre-attentive way the collimation of five (three positional and two rotational) error component widgets (ECWs), taking advantage of three key aspects: component separation and reification, error visual amplification, and dynamic hiding of the collimated components. We compared the ACW with the golden standard in a within-subjects (N=30) user test using 32 implant targets, measuring the time, error, and usability. ACW performed significantly better in positional (+19%) and angular (+47%) precision accuracy and with less mental demand (-6%) and frustration (-13%), but with an expected increase in task time (+59%) and physical demand (+64%). The interview indicated the ACW as the main preference and aesthetically more pleasant than GSW, candidating it as the new golden standard for implantology, but also for other applications where 5DOF positioning is key.
Less is More: A Simple yet Effective Token Reduction Method for Efficient Multi-modal LLMs
Venue: arXiv cs.CL 2024
Year: 2024

The rapid advancement of Multimodal Large Language Models (MLLMs) has led to remarkable performances across various domains. However, this progress is accompanied by a substantial surge in the resource consumption of these models. We address this pressing issue by introducing a new approach, Token Reduction using CLIP Metric (TRIM), aimed at improving the efficiency of MLLMs without sacrificing their performance. Inspired by human attention patterns in Visual Question Answering (VQA) tasks, TRIM presents a fresh perspective on the selection and reduction of image tokens. The TRIM method has been extensively tested across 12 datasets, and the results demonstrate a significant reduction in computational overhead while maintaining a consistent level of performance. This research marks a critical stride in efficient MLLM development, promoting greater accessibility and sustainability of high-performing models.
Introducing Quantification into a Hierarchical Graph Rewriting Language
Venue: arXiv cs.PL 2024
Year: 2024

LMNtal is a programming and modeling language based on hierarchical graph rewriting that uses logical variables to represent connectivity and membranes to represent hierarchy. On the theoretical side, it allows logical interpretation based on intuitionistic linear logic; on the practical side, its full-fledged implementation supports a graph-based parallel model checker and has been used to model diverse applications including various computational models. This paper discuss how we extend LMNtal to QLMNtal (LMNtal with Quantification) to further enhance the usefulness of hierarchical graph rewriting for high-level modeling by introducing quantifiers into rewriting as well as matching. Those quantifiers allows us to express universal quantification, cardinality and non-existence in an integrated manner. Unlike other attempts to introduce quantifiers into graph rewriting, QLMNtal has term-based syntax, whose semantics is smoothly integrated into the small-step semantics of the base language LMNtal. The proposed constructs allow combined and nested use of quantifiers within individual rewrite rules.
Estimating the distribution of numerosity and non-numerical visual magnitudes in natural scenes using computer vision
Venue: arXiv cs.CV 2024
Year: 2024

Humans share with many animal species the ability to perceive and approximately represent the number of objects in visual scenes. This ability improves throughout childhood, suggesting that learning and development play a key role in shaping our number sense. This hypothesis is further supported by computational investigations based on deep learning, which have shown that numerosity perception can spontaneously emerge in neural networks that learn the statistical structure of images with a varying number of items. However, neural network models are usually trained using synthetic datasets that might not faithfully reflect the statistical structure of natural environments, and there is also growing interest in using more ecological visual stimuli to investigate numerosity perception in humans. In this work, we exploit recent advances in computer vision algorithms to design and implement an original pipeline that can be used to estimate the distribution of numerosity and non-numerical magnitudes in large-scale datasets containing thousands of real images depicting objects in daily life situations. We show that in natural visual scenes the frequency of appearance of different numerosities follows a power law distribution. Moreover, we show that the correlational structure for numerosity and continuous magnitudes is stable across datasets and scene types (homogeneous vs. heterogeneous object sets). We suggest that considering such "ecological" pattern of covariance is important to understand the influence of non-numerical visual cues on numerosity judgements.
Selective algorithm processing of subset sum distributions
Venue: arXiv cs.DS 2024
Year: 2024

The efficiency of exact subset sum problem algorithms which compute individual subset sums is defined as $e=min(T/z, 1)$, where $z$ is the number of subset sums computed. $e$ is related to these algorithms' computational complexity. This system maps the sums into $kn$ bins to select its most efficient algorithm for each bin for each input value. These algorithms include additive, subtractive and repeated value dynamic programming. Cases which would otherwise be processed inefficiently (eg: all even values) are handled by modular arithmetic and by dynamically partioning the input values. The system's experimentally validated efficiency corresponds to O(max($T$, $n^2$)) with space complexity O(max($T$, $n$)), for $k=2$.
Deep Learning tools to support deforestation monitoring in the Ivory Coast using SAR and Optical satellite imagery
Venue: arXiv cs.CV 2024
Year: 2024

Deforestation is gaining an increasingly importance due to its strong influence on the sorrounding environment, especially in developing countries where population has a disadvantaged economic condition and agriculture is the main source of income. In Ivory Coast, for instance, where the cocoa production is the most remunerative activity, it is not rare to assist to the replacement of portion of ancient forests with new cocoa plantations. In order to monitor this type of deleterious activities, satellites can be employed to recognize the disappearance of the forest to prevent it from expand its area of interest. In this study, Forest-Non-Forest map (FNF) has been used as ground truth for models based on Sentinel images input. State-of-the-art models U-Net, Attention U-Net, Segnet and FCN32 are compared over different years combining Sentinel-1, Sentinel-2 and cloud probability to create forest/non-forest segmentation. Although Ivory Coast lacks of forest coverage datasets and is partially covered by Sentinel images, it is demonstrated the feasibility to create models classifying forest and non-forests pixels over the area using open datasets to predict where deforestation could have occurred. Although a significant portion of the deforestation research is carried out on visible bands, SAR acquisitions are employed to overcome the limits of RGB images over areas often covered by clouds. Finally, the most promising model is employed to estimate the hectares of forest has been cut between 2019 and 2020.